We follow [1]} to choose the noise distribution as the isotropic normal, i.e., \(p_z\left(z\right) \triangleq {\mathcal {N}}\left({\mathbf {0}}, \sigma _{\mathrm {noise}}^2 {\mathbf {I}}\right)\) , where the default setting of the dimension of \(z\)  is \(\mathrm {dim}\left(z\right) = {\tt min(10, \, state\_dim // 2)}\)  and of the noise standard deviation is \(\sigma _{\mathrm {noise}} = 1\) .
In the forward pass of the implicit policy, for a given state \(s\) , a noise sample \(z \sim p_z(z)\)  is first independently drawn.
Then \(s\)  is concatenated with \(z\)  and the resulting \(\left[s, z\right]\)  is inputted into the (deterministic) policy network to sample stochastic action.
